{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EMBER Cleaned — Malware Classification Dataset\n", "\n", "## Goal of this notebook\n", "\n", "This notebook takes a pre-cleaned dataset of Windows executable (PE) files,\n", "explores it, and demonstrates that a machine learning model can distinguish\n", "legitimate software from malware with over 94% accuracy — using only the\n", "static characteristics of files, without ever executing them.\n", "\n", "### Why this matters\n", "\n", "Traditional malware detection relies on \"signatures\" — known byte sequences\n", "tied to specific viruses. The problem is that every new malware variant needs\n", "a new signature. The machine learning approach is different: instead of looking\n", "for specific signatures, the model learns to recognise **general patterns**\n", "that separate benign from malicious software (e.g. unusually high entropy in\n", "the binary, imports of certain Windows APIs used for code injection, anomalies\n", "in the PE header).\n", "\n", "### What the dataset contains\n", "\n", "**EMBER** (Endgame Malware Benchmark for Research) is a public dataset created\n", "by Endgame (now part of Elastic). It contains roughly **800,000 labelled samples**\n", "(half benign, half malicious) and ~200,000 unlabelled ones. Each sample is not\n", "the original executable but a vector of **2,099 numerical features** extracted\n", "statically from the PE file — i.e. without running it. Features describe the\n", "header, sections, imported DLLs, byte histograms, entropy profile, and more.\n", "\n", "### Notebook structure\n", "\n", "1. **Loading** — how to read the files efficiently (the dataset is 6.4 GB)\n", "2. **Exploratory Data Analysis (EDA)** — label distribution, feature quality, discriminative signal\n", "3. **Use Case 1** — which features matter most for telling benign from malicious apart\n", "4. **Use Case 2** — full model evaluation: how many errors, what kind, and how to tune them" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## File Structure\n", "\n", "```\n", "ember_cleaned/\n", "├── ember_clean.npz # Index file (row/feature counts, file references)\n", "├── ember_clean_X.npy # Feature matrix — float32, raw memmap (799,838 × 2,099)\n", "├── ember_clean_y.npy # Label vector — int32 (0 = benign, 1 = malicious)\n", "├── ember_clean_metadata.parquet # Per-sample metadata (SHA-256, timestamps, families, flags)\n", "├── ember_unlabeled.npz # Index for unlabeled split (199,966 samples)\n", "├── ember_unlabeled_X.npy # Unlabeled features (same 2,099 dimensions)\n", "├── ember_unlabeled_y.npy # Label marker array (int32) for the unlabeled split; expected to contain only -1 values\n", "└── ember_clean_metadata_unlabeled.parquet # Unlabeled metadata\n", "```\n", "\n", "The `.npz` index records `_rows` and `_features` for reliable loading.\n", "The feature matrix `X` is a raw binary file (no `.npy` header) written via `np.memmap(..., mode='w+')`,\n", "so it must be read with `np.memmap` specifying `shape` and `dtype` explicitly.\n", "\n", "The **unlabeled split** is stored separately and can be used for semi-supervised\n", "learning experiments." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.metrics import (\n", " classification_report,\n", " confusion_matrix,\n", " ConfusionMatrixDisplay,\n", " roc_auc_score,\n", " roc_curve,\n", ")\n", "\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "sns.set_theme(style=\"whitegrid\")\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the Data\n", "\n", "The dataset is made of three main files:\n", "\n", "| File | Content | Size |\n", "|------|---------|------|\n", "| `ember_clean.npz` | **Index**: stores the row and column counts, needed to open the matrix with the right dimensions | ~1 KB |\n", "| `ember_clean_X.npy` | **Feature matrix**: each row is a sample (one PE file), each column is a numerical feature | ~6.4 GB |\n", "| `ember_clean_metadata.parquet` | **Metadata**: SHA-256 hash, first-seen date, malware family, label (benign/malicious) | ~50 MB |\n", "\n", "### Why we use memory-mapping\n", "\n", "The feature matrix weighs 6.4 GB — too much to load entirely into RAM on many\n", "machines. We use a technique called **memory-mapping** (`np.memmap`): the file\n", "stays on disk, but NumPy \"sees\" it as if it were an in-memory array. When we\n", "access a row, only that row is actually read from disk. This lets us work with\n", "the full dataset even on machines with limited RAM.\n", "\n", "Loading happens in three steps:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NPZ index loaded:\n", " Samples: 799,838\n", " Features: 2,099\n", " Referenced files: X=ember_clean_X.npy, y=ember_clean_y.npy\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "DATA_DIR = Path(\".\") # adjust to your dataset directory\n", "\n", "# Step 1: load the NPZ index (row/column counts)\n", "index = np.load(DATA_DIR / \"ember_clean.npz\", allow_pickle=True)\n", "n_rows = int(index[\"_rows\"])\n", "n_features = int(index[\"_features\"])\n", "\n", "print(f\"NPZ index loaded:\")\n", "print(f\" Samples: {n_rows:,}\")\n", "print(f\" Features: {n_features:,}\")\n", "print(f\" Referenced files: X={index['_X_file']}, y={index['_y_file']}\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature matrix opened: 799,838 samples × 2,099 features\n", "dtype: float32 — memory-mapped (not fully loaded into RAM)\n" ] } ], "source": [ "# Step 2: memory-map the feature matrix (stays on disk, read on demand)\n", "X = np.memmap(\n", " DATA_DIR / \"ember_clean_X.npy\",\n", " dtype=np.float32,\n", " mode=\"r\",\n", " shape=(n_rows, n_features),\n", " order=\"C\", # row-major\n", ")\n", "print(f\"Feature matrix opened: {X.shape[0]:,} samples × {X.shape[1]:,} features\")\n", "print(f\"dtype: {X.dtype} — memory-mapped (not fully loaded into RAM)\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Metadata loaded: 799,838 rows × 18 columns\n", "Labels: 399,971 benign | 399,867 malicious\n", "\n", "✅ All checks passed: 799,838 samples × 2,099 features, labels ∈ {0, 1}\n" ] } ], "source": [ "# Step 3: read metadata and extract labels\n", "meta = pd.read_parquet(DATA_DIR / \"ember_clean_metadata.parquet\")\n", "y = meta[\"label_int\"].values\n", "\n", "assert X.shape[0] == len(y), f\"Row mismatch: X={X.shape[0]}, y={len(y)}\"\n", "assert set(np.unique(y)) == {0, 1}, f\"Unexpected labels: {np.unique(y)}\"\n", "\n", "print(f\"Metadata loaded: {meta.shape[0]:,} rows × {meta.shape[1]} columns\")\n", "print(f\"Labels: {(y == 0).sum():,} benign | {(y == 1).sum():,} malicious\")\n", "print(f\"\\n✅ All checks passed: {n_rows:,} samples × {n_features:,} features, labels ∈ {{0, 1}}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Data Analysis (EDA)\n", "\n", "Before training any model, we need to understand *what is in the data*.\n", "This section answers four questions:\n", "\n", "1. **How many samples per class?** — If one class were much larger than the other,\n", " the model could \"cheat\" by always predicting the dominant class. Checking\n", " class balance is the first quality control.\n", "\n", "2. **Are the features full or empty?** — Many PE features are zero for most samples\n", " (e.g. a flag that says \"this executable imports DLL X\" is 0 for every program\n", " that does not use it). Understanding how many features are \"full\" vs \"empty\"\n", " tells us how much of the feature vector is actually informative.\n", "\n", "3. **Which regions of the matrix are active?** — The 2,099 features are not random:\n", " the first ones are header characteristics, the middle ones are import/export\n", " DLL flags, the last ones are byte and entropy histograms. Visualising the\n", " activation profile shows which feature blocks are dense and which are sparse.\n", "\n", "4. **Do the features actually separate benign from malicious?** — We check whether\n", " the distribution of a feature differs between the two groups. If it does, the\n", " model will be able to use it for classification. If not, it is noise." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The labelled split (this notebook) is nearly perfectly balanced:\n", " 399,971 benign (50.0%)\n", " 399,867 malicious (50.0%)\n", "\n", "The 199,966 unlabeled samples live in a separate file and can be used for semi-supervised experiments.\n" ] } ], "source": [ "n_benign = (y == 0).sum()\n", "n_malicious = (y == 1).sum()\n", "\n", "# Try to load unlabeled count from companion NPZ; fall back to known pipeline value\n", "n_unlabeled = 0\n", "for p in [DATA_DIR / \"ember_unlabeled.npz\",\n", " DATA_DIR / \"phase2\" / \"ember_unlabeled.npz\"]:\n", " if p.exists():\n", " n_unlabeled = int(np.load(p, allow_pickle=True).get(\"_rows\", 0))\n", " break\n", "if n_unlabeled == 0:\n", " n_unlabeled = 199_966 # 999,804 deduped − 799,838 labeled\n", "\n", "categories = [\"Unlabeled\\n(separate file)\", \"Benign\", \"Malicious\"]\n", "values = [n_unlabeled, n_benign, n_malicious]\n", "colors = [\"#9e9e9e\", \"#4caf50\", \"#f44336\"]\n", "total = sum(values)\n", "\n", "fig, ax = plt.subplots(figsize=(9, 5))\n", "bars = ax.bar(categories, values, color=colors, edgecolor=\"black\",\n", " linewidth=0.8, width=0.55)\n", "\n", "for bar, count in zip(bars, values):\n", " pct = 100 * count / total\n", " ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() * 0.90,\n", " f\"{count:,}\\n({pct:.1f}%)\", ha=\"center\", va=\"top\",\n", " fontsize=11, fontweight=\"bold\", color=\"white\")\n", "\n", "ax.set_ylabel(\"Number of samples\")\n", "ax.set_title(f\"Label distribution — full deduped dataset ({total:,} samples)\",\n", " fontsize=12, pad=15)\n", "ax.set_ylim(0, max(values) * 1.12)\n", "ax.grid(axis=\"y\", alpha=0.3)\n", "sns.despine(left=True)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"The labelled split (this notebook) is nearly perfectly balanced:\")\n", "print(f\" {n_benign:,} benign ({100*n_benign/(n_benign+n_malicious):.1f}%)\")\n", "print(f\" {n_malicious:,} malicious ({100*n_malicious/(n_benign+n_malicious):.1f}%)\")\n", "print(f\"\\nThe {n_unlabeled:,} unlabeled samples live in a separate file\"\n", " f\" and can be used for semi-supervised experiments.\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sampled 50,000 rows for EDA (from 799,838 total)\n" ] } ], "source": [ "sample_size = 50_000\n", "rng = np.random.default_rng(42)\n", "sample_idx = rng.choice(len(y), size=min(sample_size, len(y)), replace=False)\n", "X_sample = np.array(X[sample_idx]) # copy selected rows into RAM\n", "y_sample = y[sample_idx]\n", "\n", "nonzero_frac = (X_sample != 0).mean(axis=0)\n", "\n", "feat_stats = pd.DataFrame({\n", " \"mean\": X_sample.mean(axis=0),\n", " \"std\": X_sample.std(axis=0),\n", " \"nonzero_frac\": nonzero_frac,\n", "})\n", "\n", "print(f\"Sampled {len(sample_idx):,} rows for EDA (from {len(y):,} total)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature sparsity profile\n", "\n", "The chart below shows, for each of the 2,099 features (X axis), the fraction of\n", "samples where that feature has a value other than zero (Y axis).\n", "\n", "**How to read it:**\n", "\n", "- A tall bar (close to 1.0) means the feature is **dense** — it is active for\n", " almost every file. Example: \"file size\" — every PE file has a size, so this\n", " feature is always > 0.\n", "- A short bar (close to 0) means the feature is **sparse** — it fires for very\n", " few files. Example: \"imports xyz.dll\" — only a handful of programs use that DLL.\n", "\n", "The dashed lines mark the 10% and 50% thresholds. Features below 10% (red) are\n", "very sparse — useful for catching specific malware families but risky for\n", "overfitting. Features above 50% (orange) are the dense \"backbone\" of the dataset\n", "that the model relies on for baseline classification.\n", "\n", "**Why it matters:** if the vast majority of features were zero, the dataset would\n", "be almost empty and uninformative. Instead we see a healthy mix of sparse and\n", "dense features — the dataset is information-rich." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Sparse features (<10% active): 1154\n", "Dense features (>50% active): 585\n", "\n", "Note: the dense block on the right (~features 1800+) corresponds to\n", "byte and entropy histograms — they are always filled because every\n", "file has a byte distribution and an entropy level.\n" ] } ], "source": [ "fig, ax = plt.subplots(figsize=(14, 3.5))\n", "ax.bar(range(n_features), feat_stats[\"nonzero_frac\"], width=1.0,\n", " color=\"steelblue\", alpha=0.7)\n", "ax.axhline(0.10, color=\"red\", ls=\"--\", lw=0.8, label=\"10% threshold\")\n", "ax.axhline(0.50, color=\"orange\", ls=\"--\", lw=0.8, label=\"50% threshold\")\n", "ax.set_xlabel(\"Feature index (0–2098)\")\n", "ax.set_ylabel(\"Fraction of samples with value ≠ 0\")\n", "ax.set_title(\"Sparsity profile — for each feature, how many samples 'use' it?\")\n", "ax.set_xlim(0, n_features)\n", "ax.legend(fontsize=9, loc=\"upper right\")\n", "sns.despine(left=True)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"Sparse features (<10% active): {(feat_stats['nonzero_frac'] < 0.10).sum()}\")\n", "print(f\"Dense features (>50% active): {(feat_stats['nonzero_frac'] > 0.50).sum()}\")\n", "print(f\"\\nNote: the dense block on the right (~features 1800+) corresponds to\")\n", "print(f\"byte and entropy histograms — they are always filled because every\")\n", "print(f\"file has a byte distribution and an entropy level.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Per-class feature distributions\n", "\n", "The histograms below overlay benign (green) and malicious (red) for the first\n", "8 features. This is a visual sanity check: if the two \"humps\" are separated,\n", "the feature carries discriminative signal and the model can use it. If they\n", "overlap completely, the feature is noise for classification purposes.\n", "\n", "**Why the first 8?** This is an exploratory choice — we show the first few to\n", "give a visual sense. The model will use *all* 2,099 features, not just these.\n", "\n", "**The Kolmogorov-Smirnov (KS) test** below the chart quantifies this visually.\n", "It compares the shapes of the two distributions and produces two numbers:\n", "\n", "- **KS statistic** (0–1): how different the distributions are. 0 = identical,\n", " 1 = completely separated.\n", "- **p-value**: the probability that the observed difference is due to chance.\n", " If p < 0.001, we are >99.9% confident the difference is real.\n", "\n", "\"Signal = YES\" means: this feature genuinely distinguishes the two classes." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Kolmogorov-Smirnov test (first 8 features):\n", " Feature KS stat p-value Signal?\n", " ────────── ──────── ──────────── ──────────\n", " Feature 0 0.1041 2.16e-118 YES (strong)\n", " Feature 1 0.0933 4.33e-95 YES (strong)\n", " Feature 2 0.1172 4.66e-150 YES (strong)\n", " Feature 3 0.1273 5.88e-177 YES (strong)\n", " Feature 4 0.1150 1.70e-144 YES (strong)\n", " Feature 5 0.1610 1.50e-283 YES (strong)\n", " Feature 6 0.1122 1.50e-137 YES (strong)\n", " Feature 7 0.1755 0.00e+00 YES (strong)\n", "\n", "Features marked 'YES' have statistically different distributions\n", "between benign and malicious — the model can exploit them.\n" ] } ], "source": [ "fig, axes = plt.subplots(2, 4, figsize=(16, 6))\n", "for i, ax in enumerate(axes.flat):\n", " vals_b = X_sample[y_sample == 0, i]\n", " vals_m = X_sample[y_sample == 1, i]\n", " ax.hist(vals_b, bins=50, alpha=0.5, label=\"Benign\", color=\"#4caf50\", density=True)\n", " ax.hist(vals_m, bins=50, alpha=0.5, label=\"Malicious\", color=\"#f44336\", density=True)\n", " ax.set_title(f\"Feature {i}\", fontsize=10)\n", " ax.tick_params(labelsize=8)\n", " if i == 0:\n", " ax.legend(fontsize=8)\n", "plt.suptitle(\"Per-class distributions — benign (green) vs malicious (red)\",\n", " fontsize=12, y=1.02)\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "from scipy.stats import ks_2samp\n", "\n", "print(\"Kolmogorov-Smirnov test (first 8 features):\")\n", "print(f\" {'Feature':<10} {'KS stat':>8} {'p-value':>12} {'Signal?'}\")\n", "print(f\" {'─'*10} {'─'*8} {'─'*12} {'─'*10}\")\n", "for i in range(8):\n", " ks, pv = ks_2samp(X_sample[y_sample == 0, i], X_sample[y_sample == 1, i])\n", " signal = \"YES (strong)\" if pv < 0.001 else \"weak\" if pv < 0.05 else \"no\"\n", " print(f\" Feature {i:<3} {ks:>8.4f} {pv:>12.2e} {signal}\")\n", "\n", "print(f\"\\nFeatures marked 'YES' have statistically different distributions\")\n", "print(f\"between benign and malicious — the model can exploit them.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Which features matter most?\n", "\n", "We have 2,099 features, but they do not all contribute equally to classification.\n", "Some are critical (e.g. the file's entropy profile), others are nearly useless\n", "(e.g. a flag for a DLL that no sample imports). We want to find the **Top-25\n", "most discriminative features** — the ones the model relies on most to decide\n", "\"benign or malicious\".\n", "\n", "### The algorithm: Random Forest\n", "\n", "We use a **Random Forest**, which works like this:\n", "\n", "1. We create **100 independent decision trees**. Each tree is like a flowchart:\n", " at every node it asks a question such as \"is feature X greater than 0.5?\"\n", " and splits the samples into two branches.\n", "2. Each tree is trained on a random subset of the data (to make it different\n", " from the others).\n", "3. To classify a new sample, all 100 trees \"vote\" and the class with the most\n", " votes wins.\n", "\n", "**Why Random Forest** and not something else? Because it is the go-to first\n", "algorithm for tabular data: it needs no normalisation, handles sparse features\n", "well, and — crucially — provides a built-in measure of **feature importance**.\n", "\n", "### Gini importance\n", "\n", "When a tree decides to split samples using a feature, the \"confusion\" (mix of\n", "benign and malicious) in the node decreases. This decrease is called\n", "**Gini impurity reduction**.\n", "\n", "In practice:\n", "- If a feature perfectly separates benign from malicious at each split, its\n", " Gini importance is high.\n", "- If a feature does not help separate the classes, its importance is near zero.\n", "\n", "The final score is the average across all 100 trees. Higher importance =\n", "more useful for classification.\n", "\n", "### Experiment setup\n", "\n", "- **Balanced sample**: 50,000 benign + 50,000 malicious (100k total)\n", "- **Split**: 80% training (model learns) / 20% test (evaluation on unseen data)\n", "- **100 trees** with no depth limit (each tree grows until it perfectly\n", " classifies its training subset)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training set: 80,000 samples\n", "Test set: 20,000 samples\n", "\n", "Test accuracy: 94.44%\n", "(Out of 20,000 unseen samples, 18,888 classified correctly)\n", "CPU times: total: 8min 29s\n", "Wall time: 2min 8s\n" ] } ], "source": [ "%%time\n", "\n", "N_PER_CLASS = 50_000\n", "\n", "benign_idx = np.where(y == 0)[0]\n", "malicious_idx = np.where(y == 1)[0]\n", "\n", "rng = np.random.default_rng(42)\n", "b_sample = rng.choice(benign_idx, size=min(N_PER_CLASS, len(benign_idx)), replace=False)\n", "m_sample = rng.choice(malicious_idx, size=min(N_PER_CLASS, len(malicious_idx)), replace=False)\n", "use_idx = np.concatenate([b_sample, m_sample])\n", "\n", "X_use = np.array(X[use_idx]) # read selected rows from memmap into RAM\n", "y_use = np.concatenate([np.zeros(len(b_sample), dtype=int),\n", " np.ones(len(m_sample), dtype=int)])\n", "\n", "# stratify keeps the same benign/malicious ratio in both sets\n", "X_tr, X_te, y_tr, y_te = train_test_split(\n", " X_use, y_use, test_size=0.2, stratify=y_use, random_state=42\n", ")\n", "print(f\"Training set: {X_tr.shape[0]:,} samples\")\n", "print(f\"Test set: {X_te.shape[0]:,} samples\")\n", "\n", "rf = RandomForestClassifier(\n", " n_estimators=100,\n", " max_depth=None,\n", " n_jobs=-1,\n", " random_state=42,\n", ")\n", "rf.fit(X_tr, y_tr)\n", "\n", "acc = rf.score(X_te, y_te)\n", "print(f\"\\nTest accuracy: {acc:.2%}\")\n", "print(f\"(Out of 20,000 unseen samples, {int(acc*20000):,} classified correctly)\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "importances = rf.feature_importances_\n", "top_k = 10\n", "top_idx = np.argsort(importances)[::-1][:top_k]\n", "\n", "fig, ax = plt.subplots(figsize=(10, 4))\n", "ax.barh(range(top_k), importances[top_idx][::-1], color=\"darkorange\")\n", "ax.set_yticks(range(top_k))\n", "ax.set_yticklabels([f\"Feature {i}\" for i in top_idx[::-1]], fontsize=9)\n", "ax.set_xlabel(\"Gini importance (higher = more useful for classification)\", fontsize=\"10\")\n", "ax.set_title(f\"Top {top_k} most important features — model accuracy: {acc:.1%}\")\n", "plt.tight_layout()\n", "\n", "for rank, (bar, idx) in enumerate(zip(ax.patches, top_idx[::-1])):\n", " ax.text(bar.get_width() + 0.0005, bar.get_y() + bar.get_height()/2,\n", " f\"{importances[idx]:.4f}\", va=\"center\", fontsize=8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to read the feature importance chart\n", "\n", "Each bar represents a feature. The longer the bar, the more useful that feature\n", "was for the model to separate benign from malicious. But **what are** these\n", "features in concrete terms?\n", "\n", "EMBER features follow the schema of the\n", "[Elastic/EMBER extraction code](https://github.com/elastic/ember).\n", "Feature indices map to specific groups:\n", "\n", "| Feature indices | What they represent | Why they matter |\n", "|-----------------|---------------------|-----------------|\n", "| **~2,077–2,098** | **Byte-entropy histogram** bins. Entropy measures how \"random\" the data looks. | Packed or encrypted malware has high, uniform entropy — very different from legitimate software, which has structured code (medium entropy) and data sections (low entropy). |\n", "| **~497–511** | **Windows API import flags**: whether the file imports specific OS functions. | Functions like `VirtualAlloc`, `CreateRemoteThread`, `WriteProcessMemory` are used for code injection and unpacking — techniques common in malware but rare in legitimate software. |\n", "| **~0–60** | **PE header fields**: file size, number of sections, subsystem, compilation timestamp. | Anomalous values (e.g. an .exe with 0 sections, or a timestamp set in the future) indicate hand-crafted or obfuscated executables — typical of malware. |\n", "\n", "The model does not \"know\" what these features mean — it only sees numbers. But\n", "the fact that the most important features correspond to known malware indicators\n", "confirms that the model is learning real patterns, not artefacts." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How good is the model? (and where does it fail)\n", "\n", "Saying \"the model is 94% accurate\" is a good start, but it does not tell the\n", "whole story. In cybersecurity, the two types of error have very different costs:\n", "\n", "- **False positive** (benign software flagged as malware): the SOC analyst wastes\n", " time investigating a false alarm. Annoying, but not dangerous.\n", "- **False negative** (malware classified as benign): the malware slips through\n", " and can infect the network. Potentially catastrophic.\n", "\n", "This is why we need three complementary tools, not just one number:\n", "\n", "### 1. Classification Report — Precision and Recall\n", "\n", "- **Precision** (for the \"malicious\" class): of all samples the model flagged\n", " as malware, how many actually are? If precision is 96%, then 4% are false alarms.\n", "- **Recall** (for the \"malicious\" class): of all real malware in the dataset,\n", " how many did the model catch? If recall is 93%, then 7% slipped through.\n", "- **F1-score**: the harmonic mean of precision and recall — a single number\n", " that accounts for both.\n", "\n", "### 2. Confusion matrix — the 4 possible outcomes\n", "\n", "A 2×2 table showing exactly how many samples end up in each scenario:\n", "\n", "| | Predicted Benign | Predicted Malicious |\n", "|--|------------------|---------------------|\n", "| **Actually Benign** | True Negative (correct) | False Positive (false alarm) |\n", "| **Actually Malicious** | False Negative (malware missed!) | True Positive (correct) |\n", "\n", "### 3. ROC curve and AUC\n", "\n", "The model does not just produce \"benign\" or \"malicious\": it produces a\n", "**probability** (e.g. \"I am 73% confident this is malware\"). The default\n", "threshold is 0.5 — above → malicious, below → benign. But we can raise or\n", "lower it.\n", "\n", "The **ROC curve** (Receiver Operating Characteristic) shows what happens when\n", "we move this threshold:\n", "- Y axis = **True Positive Rate** (how much malware we catch)\n", "- X axis = **False Positive Rate** (how many false alarms we generate)\n", "\n", "A curve that climbs steeply to the top-left = excellent model (catches almost\n", "all malware with very few false alarms). A diagonal = useless model (equivalent\n", "to flipping a coin).\n", "\n", "**AUC** (Area Under the Curve) is the area under this curve:\n", "- **AUC = 1.0** → perfect model\n", "- **AUC = 0.5** → random classifier (useless)\n", "- **AUC > 0.95** → excellent model (our case)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=== Classification Report ===\n", "\n", " precision recall f1-score support\n", "\n", " Benign 0.93 0.96 0.95 10000\n", " Malicious 0.96 0.93 0.94 10000\n", "\n", " accuracy 0.94 20000\n", " macro avg 0.94 0.94 0.94 20000\n", "weighted avg 0.94 0.94 0.94 20000\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AUC = 0.9889\n" ] } ], "source": [ "y_pred = rf.predict(X_te) # hard predictions: 0 or 1\n", "y_prob = rf.predict_proba(X_te)[:, 1] # probability of being malicious (0.0–1.0)\n", "\n", "print(\"=== Classification Report ===\\n\")\n", "print(classification_report(y_te, y_pred, target_names=[\"Benign\", \"Malicious\"]))\n", "\n", "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", "\n", "# Left: confusion matrix\n", "cm = confusion_matrix(y_te, y_pred)\n", "disp = ConfusionMatrixDisplay(cm, display_labels=[\"Benign\", \"Malicious\"])\n", "disp.plot(ax=axes[0], cmap=\"Blues\", values_format=\",\")\n", "axes[0].set_title(\"Confusion Matrix\")\n", "axes[0].set_xlabel(\"Model prediction\")\n", "axes[0].set_ylabel(\"True label\")\n", "\n", "# Right: ROC curve\n", "fpr, tpr, _ = roc_curve(y_te, y_prob)\n", "roc_auc = roc_auc_score(y_te, y_prob)\n", "axes[1].plot(fpr, tpr, lw=2, color=\"steelblue\",\n", " label=f\"Random Forest (AUC = {roc_auc:.4f})\")\n", "axes[1].plot([0, 1], [0, 1], \"k--\", lw=1, label=\"Random classifier (AUC = 0.5)\")\n", "axes[1].set_xlabel(\"False Positive Rate (false alarms)\")\n", "axes[1].set_ylabel(\"True Positive Rate (malware caught)\")\n", "axes[1].set_title(\"ROC Curve\")\n", "axes[1].legend(loc=\"lower right\")\n", "axes[1].fill_between(fpr, tpr, alpha=0.1, color=\"steelblue\")\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"AUC = {roc_auc:.4f}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Operational implications — how to use this in practice\n", "\n", "The classification threshold (default 0.5) can be tuned depending on context:\n", "\n", "| Threshold | What changes | When to use it |\n", "|-----------|--------------|----------------|\n", "| **Low** (e.g. 0.2) | Catches nearly all malware, but generates more false alarms | Critical infrastructure (hospitals, defence) — better one extra alert than one malware in the network |\n", "| **Default** (0.5) | Balanced trade-off between false alarms and missed malware | General-purpose EDR (Endpoint Detection & Response) products |\n", "| **High** (e.g. 0.9) | Almost zero false alarms, but lets more malware through | App stores, user-facing alerts — false positives erode product trust |\n", "\n", "### What the AUC tells us\n", "\n", "An AUC of ~0.99 means the model separates benign from malicious excellently\n", "using **static features alone** (without executing the files). In a real\n", "deployment, these scores are combined with dynamic analysis (sandboxing) and\n", "threat-intelligence feeds to further reduce both types of error." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 4 }